FigureS1E

Load Data

dataset <- read.delim("raw_data/FigureS1E.txt", stringsAsFactors = FALSE)

dataset$genotype <- gsub(" ", "", dataset$genotype )
dataset$genotype <- factor(dataset$genotype)
dataset$Experiment <- factor(rep(paste0("exp", 1:(length(dataset$genotype)/length(levels(dataset$genotype)))), each=length(unique(dataset$genotype))))

dataset$siRNA <-  factor(gsub(".*[T,O]\\+","",dataset$genotype))
dataset$genotype <-  factor(gsub("\\+.*","",dataset$genotype))

dataset$UID <- factor(paste(dataset$Experiment, dataset$genotype, dataset$siRNA))
dataset$GSID <- factor(paste(dataset$genotype, dataset$siRNA))

# wide format
kable(dataset, row.names = F)

genotype	NT	olaparib_30nM	olaparib_300nM	olaparib_3000nM	Experiment	siRNA	UID	GSID
WT	127	120	87	33	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl
WT	189	159	157	67	exp1	siPARP1	exp1 WT siPARP1	WT siPARP1
ALC1KO	148	48	25	18	exp1	siCtrl	exp1 ALC1KO siCtrl	ALC1KO siCtrl
ALC1KO	171	148	126	52	exp1	siPARP1	exp1 ALC1KO siPARP1	ALC1KO siPARP1
WT	250	241	166	77	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl
WT	256	217	197	123	exp2	siPARP1	exp2 WT siPARP1	WT siPARP1
ALC1KO	213	91	29	8	exp2	siCtrl	exp2 ALC1KO siCtrl	ALC1KO siCtrl
ALC1KO	224	193	132	69	exp2	siPARP1	exp2 ALC1KO siPARP1	ALC1KO siPARP1
WT	402	388	247	127	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl
WT	352	310	250	141	exp3	siPARP1	exp3 WT siPARP1	WT siPARP1
ALC1KO	213	82	37	19	exp3	siCtrl	exp3 ALC1KO siCtrl	ALC1KO siCtrl
ALC1KO	297	269	148	77	exp3	siPARP1	exp3 ALC1KO siPARP1	ALC1KO siPARP1

library(reshape2)
# reshape to long format
dataset <- melt(dataset, variable.name = "Treatment", value.name = "Counts")

dataset$genotype <- relevel(dataset$genotype, ref = "WT")
dataset$siRNA <- relevel(dataset$siRNA, ref = "siCtrl")
dataset$UID <- relevel(dataset$UID, ref = "exp1 WT siCtrl")

dataset$Olaparib <- gsub("NT","1",dataset$Treatment)
dataset$Olaparib <- gsub("olaparib_|nM","",dataset$Olaparib)
dataset$Olaparib <- log10(as.integer(dataset$Olaparib))




dataset$Offset <- NA
for(uid in levels(dataset$UID)){
        dataset$Offset[dataset$UID == uid] <- mean(dataset$Counts[dataset$UID == uid])
}

dataset$NormCounts <- dataset$Counts / dataset$Offset



dataset$Offset2 <- NA
for(gsid in levels(dataset$GSID)){
        dataset$Offset2[dataset$GSID == gsid] <- mean(dataset$NormCounts[dataset$GSID == gsid & dataset$Olaparib == 0])
}

dataset$NormCounts2 <- dataset$NormCounts / dataset$Offset2



# long format
kable(dataset, row.names = F)

genotype	Experiment	siRNA	UID	GSID	Treatment	Counts	Olaparib	Offset	NormCounts	Offset2	NormCounts2
WT	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl	NT	127	0.000000	91.75	1.3841962	1.376012	1.0059474
WT	exp1	siPARP1	exp1 WT siPARP1	WT siPARP1	NT	189	0.000000	143.00	1.3216783	1.316703	1.0037786
ALC1KO	exp1	siCtrl	exp1 ALC1KO siCtrl	ALC1KO siCtrl	NT	148	0.000000	59.75	2.4769874	2.467624	1.0037946
ALC1KO	exp1	siPARP1	exp1 ALC1KO siPARP1	ALC1KO siPARP1	NT	171	0.000000	124.25	1.3762575	1.442664	0.9539695
WT	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl	NT	250	0.000000	183.50	1.3623978	1.376012	0.9901057
WT	exp2	siPARP1	exp2 WT siPARP1	WT siPARP1	NT	256	0.000000	198.25	1.2912989	1.316703	0.9807062
ALC1KO	exp2	siCtrl	exp2 ALC1KO siCtrl	ALC1KO siCtrl	NT	213	0.000000	85.25	2.4985337	2.467624	1.0125262
ALC1KO	exp2	siPARP1	exp2 ALC1KO siPARP1	ALC1KO siPARP1	NT	224	0.000000	154.50	1.4498382	1.442664	1.0049729
WT	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl	NT	402	0.000000	291.00	1.3814433	1.376012	1.0039468
WT	exp3	siPARP1	exp3 WT siPARP1	WT siPARP1	NT	352	0.000000	263.25	1.3371320	1.316703	1.0155152
ALC1KO	exp3	siCtrl	exp3 ALC1KO siCtrl	ALC1KO siCtrl	NT	213	0.000000	87.75	2.4273504	2.467624	0.9836793
ALC1KO	exp3	siPARP1	exp3 ALC1KO siPARP1	ALC1KO siPARP1	NT	297	0.000000	197.75	1.5018963	1.442664	1.0410576
WT	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl	olaparib_30nM	120	1.477121	91.75	1.3079019	1.376012	0.9505015
WT	exp1	siPARP1	exp1 WT siPARP1	WT siPARP1	olaparib_30nM	159	1.477121	143.00	1.1118881	1.316703	0.8444486
ALC1KO	exp1	siCtrl	exp1 ALC1KO siCtrl	ALC1KO siCtrl	olaparib_30nM	48	1.477121	59.75	0.8033473	2.467624	0.3255550
ALC1KO	exp1	siPARP1	exp1 ALC1KO siPARP1	ALC1KO siPARP1	olaparib_30nM	148	1.477121	124.25	1.1911469	1.442664	0.8256579
WT	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl	olaparib_30nM	241	1.477121	183.50	1.3133515	1.376012	0.9544619
WT	exp2	siPARP1	exp2 WT siPARP1	WT siPARP1	olaparib_30nM	217	1.477121	198.25	1.0945776	1.316703	0.8313017
ALC1KO	exp2	siCtrl	exp2 ALC1KO siCtrl	ALC1KO siCtrl	olaparib_30nM	91	1.477121	85.25	1.0674487	2.467624	0.4325816
ALC1KO	exp2	siPARP1	exp2 ALC1KO siPARP1	ALC1KO siPARP1	olaparib_30nM	193	1.477121	154.50	1.2491909	1.442664	0.8658918
WT	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl	olaparib_30nM	388	1.477121	291.00	1.3333333	1.376012	0.9689835
WT	exp3	siPARP1	exp3 WT siPARP1	WT siPARP1	olaparib_30nM	310	1.477121	263.25	1.1775878	1.316703	0.8943458
ALC1KO	exp3	siCtrl	exp3 ALC1KO siCtrl	ALC1KO siCtrl	olaparib_30nM	82	1.477121	87.75	0.9344729	2.467624	0.3786934
ALC1KO	exp3	siPARP1	exp3 ALC1KO siPARP1	ALC1KO siPARP1	olaparib_30nM	269	1.477121	197.75	1.3603034	1.442664	0.9429107
WT	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl	olaparib_300nM	87	2.477121	91.75	0.9482289	1.376012	0.6891136
WT	exp1	siPARP1	exp1 WT siPARP1	WT siPARP1	olaparib_300nM	157	2.477121	143.00	1.0979021	1.316703	0.8338266
ALC1KO	exp1	siCtrl	exp1 ALC1KO siCtrl	ALC1KO siCtrl	olaparib_300nM	25	2.477121	59.75	0.4184100	2.467624	0.1695599
ALC1KO	exp1	siPARP1	exp1 ALC1KO siPARP1	ALC1KO siPARP1	olaparib_300nM	126	2.477121	124.25	1.0140845	1.442664	0.7029249
WT	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl	olaparib_300nM	166	2.477121	183.50	0.9046322	1.376012	0.6574302
WT	exp2	siPARP1	exp2 WT siPARP1	WT siPARP1	olaparib_300nM	197	2.477121	198.25	0.9936948	1.316703	0.7546841
ALC1KO	exp2	siCtrl	exp2 ALC1KO siCtrl	ALC1KO siCtrl	olaparib_300nM	29	2.477121	85.25	0.3401760	2.467624	0.1378557
ALC1KO	exp2	siPARP1	exp2 ALC1KO siPARP1	ALC1KO siPARP1	olaparib_300nM	132	2.477121	154.50	0.8543689	1.442664	0.5922161
WT	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl	olaparib_300nM	247	2.477121	291.00	0.8487973	1.376012	0.6168529
WT	exp3	siPARP1	exp3 WT siPARP1	WT siPARP1	olaparib_300nM	250	2.477121	263.25	0.9496676	1.316703	0.7212466
ALC1KO	exp3	siCtrl	exp3 ALC1KO siCtrl	ALC1KO siCtrl	olaparib_300nM	37	2.477121	87.75	0.4216524	2.467624	0.1708739
ALC1KO	exp3	siPARP1	exp3 ALC1KO siPARP1	ALC1KO siPARP1	olaparib_300nM	148	2.477121	197.75	0.7484197	1.442664	0.5187762
WT	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl	olaparib_3000nM	33	3.477121	91.75	0.3596730	1.376012	0.2613879
WT	exp1	siPARP1	exp1 WT siPARP1	WT siPARP1	olaparib_3000nM	67	3.477121	143.00	0.4685315	1.316703	0.3558368
ALC1KO	exp1	siCtrl	exp1 ALC1KO siCtrl	ALC1KO siCtrl	olaparib_3000nM	18	3.477121	59.75	0.3012552	2.467624	0.1220831
ALC1KO	exp1	siPARP1	exp1 ALC1KO siPARP1	ALC1KO siPARP1	olaparib_3000nM	52	3.477121	124.25	0.4185111	1.442664	0.2900960
WT	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl	olaparib_3000nM	77	3.477121	183.50	0.4196185	1.376012	0.3049526
WT	exp2	siPARP1	exp2 WT siPARP1	WT siPARP1	olaparib_3000nM	123	3.477121	198.25	0.6204288	1.316703	0.4711987
ALC1KO	exp2	siCtrl	exp2 ALC1KO siCtrl	ALC1KO siCtrl	olaparib_3000nM	8	3.477121	85.25	0.0938416	2.467624	0.0380292
ALC1KO	exp2	siPARP1	exp2 ALC1KO siPARP1	ALC1KO siPARP1	olaparib_3000nM	69	3.477121	154.50	0.4466019	1.442664	0.3095675
WT	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl	olaparib_3000nM	127	3.477121	291.00	0.4364261	1.376012	0.3171673
WT	exp3	siPARP1	exp3 WT siPARP1	WT siPARP1	olaparib_3000nM	141	3.477121	263.25	0.5356125	1.316703	0.4067831
ALC1KO	exp3	siCtrl	exp3 ALC1KO siCtrl	ALC1KO siCtrl	olaparib_3000nM	19	3.477121	87.75	0.2165242	2.467624	0.0877460
ALC1KO	exp3	siPARP1	exp3 ALC1KO siPARP1	ALC1KO siPARP1	olaparib_3000nM	77	3.477121	197.75	0.3893805	1.442664	0.2699038

Plot Data

library(ggplot2)

# raw data
ggplot(dataset, aes(x=Olaparib, y=Counts)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_smooth(method=lm, formula = y ~poly(x, 2), se=FALSE, aes(colour=siRNA)) +
        geom_point(aes(colour=siRNA, shape=Experiment), size=2) +        
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        scale_shape_manual(values=15:20) +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Linear
ggplot(dataset, aes(x=Olaparib, y=NormCounts, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Linear
ggplot(dataset, aes(x=Olaparib, y=NormCounts2, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Quadratic
ggplot(dataset, aes(x=Olaparib, y=NormCounts, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~poly(x, 2), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)")+
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Quadratic
ggplot(dataset, aes(x=Olaparib, y=NormCounts2, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~poly(x, 2), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Cubic
ggplot(dataset, aes(x=Olaparib, y=NormCounts, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x, 3), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)")+
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Cubic
ggplot(dataset, aes(x=Olaparib, y=NormCounts2, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x, 3), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

library(Cairo)


cairo_pdf("FigureS1E.pdf", width = 5, height = 4, family = "Arial")

ggplot(dataset, aes(x=Olaparib, y=NormCounts2)) + 
        theme_bw() +
        theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), 
              axis.line = element_line(colour = "black"), text = element_text(size=14),
              panel.border = element_blank(), panel.background = element_blank()) +
        geom_point(aes(colour = siRNA, shape = genotype), size=1.75) +
        geom_smooth(method=lm, formula = y ~ poly(x,3), se=TRUE, 
                    aes(group = GSID,colour = siRNA, linetype = genotype), fill='#DDDDDD', size=0.5) +
        #facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        ylab(label = "Normalized Counts") +
        scale_color_manual(values=c("#000000","#FF0000")) +
        guides(linetype = guide_legend(override.aes= list(color = "#555555"))) 

dev.off()

## quartz_off_screen 
##                 2

Models

library(MASS)
library(DHARMa)
library(lme4)
library(lmerTest)
library(bbmle)

Linear formula

fit1 <- lm(Counts ~ Experiment + Olaparib*siRNA*genotype, data = dataset)
print(summary(fit1))

## 
## Call:
## lm(formula = Counts ~ Experiment + Olaparib * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -107.230  -24.808   -4.373   23.873  126.007 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                           234.2303    24.6661   9.496 1.41e-11 ***
## Experimentexp2                         50.6875    15.9414   3.180  0.00293 ** 
## Experimentexp3                        105.2500    15.9414   6.602 8.53e-08 ***
## Olaparib                              -52.4585    10.1314  -5.178 7.61e-06 ***
## siRNAsiPARP1                           -6.6819    32.3638  -0.206  0.83753    
## genotypeALC1KO                       -112.8260    32.3638  -3.486  0.00125 ** 
## Olaparib:siRNAsiPARP1                  10.4594    14.3280   0.730  0.46987    
## Olaparib:genotypeALC1KO                 0.8931    14.3280   0.062  0.95062    
## siRNAsiPARP1:genotypeALC1KO            80.8501    45.7693   1.766  0.08535 .  
## Olaparib:siRNAsiPARP1:genotypeALC1KO   -6.6476    20.2628  -0.328  0.74466    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 45.09 on 38 degrees of freedom
## Multiple R-squared:  0.8338, Adjusted R-squared:  0.7944 
## F-statistic: 21.18 on 9 and 38 DF,  p-value: 3.24e-12

cat("AIC: ", AIC(fit1))

## AIC:  512.6341

simres <- simulateResiduals(fittedModel = fit1)
plot(simres)

fit2 <- lm(NormCounts ~ Olaparib*siRNA*genotype, data = dataset)
print(summary(fit2))

## 
## Call:
## lm(formula = NormCounts ~ Olaparib * siRNA * genotype, data = dataset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.4485 -0.1338 -0.0444  0.1440  0.3723 
## 
## Coefficients:
##                                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                           1.51901    0.09902  15.340  < 2e-16 ***
## Olaparib                             -0.27936    0.04384  -6.373 1.42e-07 ***
## siRNAsiPARP1                         -0.13321    0.14004  -0.951   0.3472    
## genotypeALC1KO                        0.70978    0.14004   5.068 9.52e-06 ***
## Olaparib:siRNAsiPARP1                 0.07170    0.06200   1.157   0.2543    
## Olaparib:genotypeALC1KO              -0.38205    0.06200  -6.162 2.79e-07 ***
## siRNAsiPARP1:genotypeALC1KO          -0.54819    0.19804  -2.768   0.0085 ** 
## Olaparib:siRNAsiPARP1:genotypeALC1KO  0.29507    0.08768   3.365   0.0017 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1951 on 40 degrees of freedom
## Multiple R-squared:  0.8936, Adjusted R-squared:  0.8749 
## F-statistic: 47.98 on 7 and 40 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit2))

## AIC:  -11.42023

simres <- simulateResiduals(fittedModel = fit2)
plot(simres)

fit3 <- lm(NormCounts2 ~ Olaparib*siRNA*genotype, data = dataset)
print(summary(fit3))

## 
## Call:
## lm(formula = NormCounts2 ~ Olaparib * siRNA * genotype, data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.18174 -0.08375 -0.03222  0.08930  0.17202 
## 
## Coefficients:
##                                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                           1.10392    0.05572  19.813  < 2e-16 ***
## Olaparib                             -0.20302    0.02467  -8.231 3.86e-10 ***
## siRNAsiPARP1                         -0.05144    0.07880  -0.653   0.5176    
## genotypeALC1KO                       -0.20071    0.07880  -2.547   0.0148 *  
## Olaparib:siRNAsiPARP1                 0.04531    0.03488   1.299   0.2014    
## Olaparib:genotypeALC1KO              -0.06501    0.03488  -1.864   0.0697 .  
## siRNAsiPARP1:genotypeALC1KO           0.22082    0.11144   1.982   0.0544 .  
## Olaparib:siRNAsiPARP1:genotypeALC1KO  0.01849    0.04933   0.375   0.7098    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1098 on 40 degrees of freedom
## Multiple R-squared:  0.9035, Adjusted R-squared:  0.8866 
## F-statistic:  53.5 on 7 and 40 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit3))

## AIC:  -66.62439

simres <- simulateResiduals(fittedModel = fit3)
plot(simres)

fit4 <- lmer(Counts ~ Olaparib*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit4))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ Olaparib * siRNA * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 444
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.90616 -0.53229  0.01726  0.51233  2.31632 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 3446     58.7    
##  Residual             1391     37.3    
## Number of obs: 48, groups:  UID, 12
## 
## Fixed effects:
##                                       Estimate Std. Error        df t value
## (Intercept)                           286.2095    38.8194   11.2590   7.373
## Olaparib                              -52.4585     8.3818   32.0000  -6.259
## siRNAsiPARP1                           -6.6819    54.8990   11.2590  -0.122
## genotypeALC1KO                       -112.8260    54.8990   11.2590  -2.055
## Olaparib:siRNAsiPARP1                  10.4594    11.8537   32.0000   0.882
## Olaparib:genotypeALC1KO                 0.8931    11.8537   32.0000   0.075
## siRNAsiPARP1:genotypeALC1KO            80.8501    77.6389   11.2590   1.041
## Olaparib:siRNAsiPARP1:genotypeALC1KO   -6.6476    16.7637   32.0000  -0.397
##                                      Pr(>|t|)    
## (Intercept)                          1.23e-05 ***
## Olaparib                             5.15e-07 ***
## siRNAsiPARP1                           0.9053    
## genotypeALC1KO                         0.0638 .  
## Olaparib:siRNAsiPARP1                  0.3842    
## Olaparib:genotypeALC1KO                0.9404    
## siRNAsiPARP1:genotypeALC1KO            0.3196    
## Olaparib:siRNAsiPARP1:genotypeALC1KO   0.6943    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Olaprb sRNAsPARP1 gALC1K Ol:RNAPARP1 O:ALC1 sRNAPARP1:
## Olaparib    -0.401                                                       
## siRNAsPARP1 -0.707  0.284                                                
## gntypALC1KO -0.707  0.284  0.500                                         
## Ol:RNAPARP1  0.284 -0.707 -0.401     -0.201                              
## Olpr:ALC1KO  0.284 -0.707 -0.201     -0.401  0.500                       
## sRNAPARP1:A  0.500 -0.201 -0.707     -0.707  0.284       0.284           
## O:RNAPARP1: -0.201  0.500  0.284      0.284 -0.707      -0.707 -0.401

cat("AIC: ", AIC(fit4))

## AIC:  464.0374

simres <- simulateResiduals(fittedModel = fit4)
plot(simres)

Quadratic formula

fit5 <- lm(Counts ~ Experiment + poly(Olaparib,2)*siRNA*genotype, data = dataset)
print(summary(fit5))

## 
## Call:
## lm(formula = Counts ~ Experiment + poly(Olaparib, 2) * siRNA * 
##     genotype, data = dataset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -82.799 -20.793   2.271  18.236  94.158 
## 
## Coefficients:
##                                                Estimate Std. Error t value
## (Intercept)                                      136.77      14.44   9.472
## Experimentexp2                                    50.69      14.44   3.510
## Experimentexp3                                   105.25      14.44   7.289
## poly(Olaparib, 2)1                              -466.93      81.68  -5.717
## poly(Olaparib, 2)2                              -186.06      81.68  -2.278
## siRNAsiPARP1                                      12.75      16.67   0.765
## genotypeALC1KO                                  -111.17      16.67  -6.668
## poly(Olaparib, 2)1:siRNAsiPARP1                   93.10     115.51   0.806
## poly(Olaparib, 2)2:siRNAsiPARP1                   63.02     115.51   0.546
## poly(Olaparib, 2)1:genotypeALC1KO                  7.95     115.51   0.069
## poly(Olaparib, 2)2:genotypeALC1KO                322.38     115.51   2.791
## siRNAsiPARP1:genotypeALC1KO                       68.50      23.58   2.905
## poly(Olaparib, 2)1:siRNAsiPARP1:genotypeALC1KO   -59.17     163.36  -0.362
## poly(Olaparib, 2)2:siRNAsiPARP1:genotypeALC1KO  -317.01     163.36  -1.941
##                                                Pr(>|t|)    
## (Intercept)                                    4.59e-11 ***
## Experimentexp2                                  0.00128 ** 
## Experimentexp3                                 1.93e-08 ***
## poly(Olaparib, 2)1                             2.01e-06 ***
## poly(Olaparib, 2)2                              0.02914 *  
## siRNAsiPARP1                                    0.44972    
## genotypeALC1KO                                 1.19e-07 ***
## poly(Olaparib, 2)1:siRNAsiPARP1                 0.42587    
## poly(Olaparib, 2)2:siRNAsiPARP1                 0.58895    
## poly(Olaparib, 2)1:genotypeALC1KO               0.94553    
## poly(Olaparib, 2)2:genotypeALC1KO               0.00856 ** 
## siRNAsiPARP1:genotypeALC1KO                     0.00641 ** 
## poly(Olaparib, 2)1:siRNAsiPARP1:genotypeALC1KO  0.71944    
## poly(Olaparib, 2)2:siRNAsiPARP1:genotypeALC1KO  0.06064 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 40.84 on 34 degrees of freedom
## Multiple R-squared:  0.878,  Adjusted R-squared:  0.8313 
## F-statistic: 18.82 on 13 and 34 DF,  p-value: 7.868e-12

cat("AIC: ", AIC(fit5))

## AIC:  505.7933

simres <- simulateResiduals(fittedModel = fit5)
plot(simres)

fit6 <- lm(NormCounts ~ poly(Olaparib,2)*siRNA*genotype, data = dataset)
print(summary(fit6))

## 
## Call:
## lm(formula = NormCounts ~ poly(Olaparib, 2) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.154949 -0.038710  0.005231  0.034999  0.154487 
## 
## Coefficients:
##                                                  Estimate Std. Error t value
## (Intercept)                                     1.000e+00  2.178e-02  45.921
## poly(Olaparib, 2)1                             -2.487e+00  1.509e-01 -16.481
## poly(Olaparib, 2)2                             -1.025e+00  1.509e-01  -6.791
## siRNAsiPARP1                                   -2.100e-16  3.080e-02   0.000
## genotypeALC1KO                                 -1.290e-16  3.080e-02   0.000
## poly(Olaparib, 2)1:siRNAsiPARP1                 6.382e-01  2.134e-01   2.991
## poly(Olaparib, 2)2:siRNAsiPARP1                 3.941e-01  2.134e-01   1.847
## poly(Olaparib, 2)1:genotypeALC1KO              -3.401e+00  2.134e-01 -15.938
## poly(Olaparib, 2)2:genotypeALC1KO               2.830e+00  2.134e-01  13.262
## siRNAsiPARP1:genotypeALC1KO                    -2.950e-17  4.355e-02   0.000
## poly(Olaparib, 2)1:siRNAsiPARP1:genotypeALC1KO  2.626e+00  3.017e-01   8.704
## poly(Olaparib, 2)2:siRNAsiPARP1:genotypeALC1KO -2.951e+00  3.017e-01  -9.779
##                                                Pr(>|t|)    
## (Intercept)                                     < 2e-16 ***
## poly(Olaparib, 2)1                              < 2e-16 ***
## poly(Olaparib, 2)2                             6.18e-08 ***
## siRNAsiPARP1                                    1.00000    
## genotypeALC1KO                                  1.00000    
## poly(Olaparib, 2)1:siRNAsiPARP1                 0.00499 ** 
## poly(Olaparib, 2)2:siRNAsiPARP1                 0.07301 .  
## poly(Olaparib, 2)1:genotypeALC1KO               < 2e-16 ***
## poly(Olaparib, 2)2:genotypeALC1KO              2.01e-15 ***
## siRNAsiPARP1:genotypeALC1KO                     1.00000    
## poly(Olaparib, 2)1:siRNAsiPARP1:genotypeALC1KO 2.21e-10 ***
## poly(Olaparib, 2)2:siRNAsiPARP1:genotypeALC1KO 1.13e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07544 on 36 degrees of freedom
## Multiple R-squared:  0.9857, Adjusted R-squared:  0.9813 
## F-statistic: 225.3 on 11 and 36 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit6))

## AIC:  -99.69902

simres <- simulateResiduals(fittedModel = fit6)
plot(simres)

fit7 <- lm(NormCounts2 ~ poly(Olaparib,2)*siRNA*genotype, data = dataset)
print(summary(fit7))

## 
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 2) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.10741 -0.02228  0.00343  0.02370  0.11733 
## 
## Coefficients:
##                                                Estimate Std. Error t value
## (Intercept)                                     0.72674    0.01398  51.966
## poly(Olaparib, 2)1                             -1.80709    0.09689 -18.651
## poly(Olaparib, 2)2                             -0.74460    0.09689  -7.685
## siRNAsiPARP1                                    0.03274    0.01978   1.655
## genotypeALC1KO                                 -0.32149    0.01978 -16.255
## poly(Olaparib, 2)1:siRNAsiPARP1                 0.40330    0.13702   2.943
## poly(Olaparib, 2)2:siRNAsiPARP1                 0.26573    0.13702   1.939
## poly(Olaparib, 2)1:genotypeALC1KO              -0.57866    0.13702  -4.223
## poly(Olaparib, 2)2:genotypeALC1KO               1.47615    0.13702  10.773
## siRNAsiPARP1:genotypeALC1KO                     0.25518    0.02797   9.123
## poly(Olaparib, 2)1:siRNAsiPARP1:genotypeALC1KO  0.16459    0.19378   0.849
## poly(Olaparib, 2)2:siRNAsiPARP1:genotypeALC1KO -1.51820    0.19378  -7.835
##                                                Pr(>|t|)    
## (Intercept)                                     < 2e-16 ***
## poly(Olaparib, 2)1                              < 2e-16 ***
## poly(Olaparib, 2)2                             4.24e-09 ***
## siRNAsiPARP1                                   0.106589    
## genotypeALC1KO                                  < 2e-16 ***
## poly(Olaparib, 2)1:siRNAsiPARP1                0.005654 ** 
## poly(Olaparib, 2)2:siRNAsiPARP1                0.060334 .  
## poly(Olaparib, 2)1:genotypeALC1KO              0.000156 ***
## poly(Olaparib, 2)2:genotypeALC1KO              8.19e-13 ***
## siRNAsiPARP1:genotypeALC1KO                    6.80e-11 ***
## poly(Olaparib, 2)1:siRNAsiPARP1:genotypeALC1KO 0.401300    
## poly(Olaparib, 2)2:siRNAsiPARP1:genotypeALC1KO 2.73e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.04845 on 36 degrees of freedom
## Multiple R-squared:  0.9831, Adjusted R-squared:  0.9779 
## F-statistic: 190.2 on 11 and 36 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit7))

## AIC:  -142.2131

simres <- simulateResiduals(fittedModel = fit7)
plot(simres)

fit8 <- lmer(Counts ~ poly(Olaparib,2)*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit8))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Olaparib, 2) * siRNA * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 367.1
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.52447 -0.58141 -0.02631  0.42330  1.74095 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 3579.2   59.83   
##  Residual              856.5   29.27   
## Number of obs: 48, groups:  UID, 12
## 
## Fixed effects:
##                                                Estimate Std. Error      df
## (Intercept)                                      188.75      35.56    8.00
## poly(Olaparib, 2)1                              -466.93      58.53   28.00
## poly(Olaparib, 2)2                              -186.06      58.53   28.00
## siRNAsiPARP1                                      12.75      50.29    8.00
## genotypeALC1KO                                  -111.17      50.29    8.00
## poly(Olaparib, 2)1:siRNAsiPARP1                   93.10      82.78   28.00
## poly(Olaparib, 2)2:siRNAsiPARP1                   63.02      82.78   28.00
## poly(Olaparib, 2)1:genotypeALC1KO                  7.95      82.78   28.00
## poly(Olaparib, 2)2:genotypeALC1KO                322.38      82.78   28.00
## siRNAsiPARP1:genotypeALC1KO                       68.50      71.12    8.00
## poly(Olaparib, 2)1:siRNAsiPARP1:genotypeALC1KO   -59.17     117.06   28.00
## poly(Olaparib, 2)2:siRNAsiPARP1:genotypeALC1KO  -317.01     117.06   28.00
##                                                t value Pr(>|t|)    
## (Intercept)                                      5.308 0.000721 ***
## poly(Olaparib, 2)1                              -7.977 1.09e-08 ***
## poly(Olaparib, 2)2                              -3.179 0.003593 ** 
## siRNAsiPARP1                                     0.254 0.806247    
## genotypeALC1KO                                  -2.211 0.058028 .  
## poly(Olaparib, 2)1:siRNAsiPARP1                  1.125 0.270268    
## poly(Olaparib, 2)2:siRNAsiPARP1                  0.761 0.452854    
## poly(Olaparib, 2)1:genotypeALC1KO                0.096 0.924173    
## poly(Olaparib, 2)2:genotypeALC1KO                3.895 0.000557 ***
## siRNAsiPARP1:genotypeALC1KO                      0.963 0.363663    
## poly(Olaparib, 2)1:siRNAsiPARP1:genotypeALC1KO  -0.505 0.617201    
## poly(Olaparib, 2)2:siRNAsiPARP1:genotypeALC1KO  -2.708 0.011410 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##                   (Intr) pl(O,2)1 pl(O,2)2 sRNAsPARP1 gALC1K pl(O,2)1:RNAPARP1
## ply(Olp,2)1        0.000                                                      
## ply(Olp,2)2        0.000  0.000                                               
## siRNAsPARP1       -0.707  0.000    0.000                                      
## gntypALC1KO       -0.707  0.000    0.000    0.500                             
## pl(O,2)1:RNAPARP1  0.000 -0.707    0.000    0.000      0.000                  
## pl(O,2)2:RNAPARP1  0.000  0.000   -0.707    0.000      0.000  0.000           
## p(O,2)1:ALC        0.000 -0.707    0.000    0.000      0.000  0.500           
## p(O,2)2:ALC        0.000  0.000   -0.707    0.000      0.000  0.000           
## sRNAPARP1:A        0.500  0.000    0.000   -0.707     -0.707  0.000           
## p(O,2)1:RNAPARP1:  0.000  0.500    0.000    0.000      0.000 -0.707           
## p(O,2)2:RNAPARP1:  0.000  0.000    0.500    0.000      0.000  0.000           
##                   pl(O,2)2:RNAPARP1 p(O,2)1:A p(O,2)2:A sRNAPARP1:
## ply(Olp,2)1                                                       
## ply(Olp,2)2                                                       
## siRNAsPARP1                                                       
## gntypALC1KO                                                       
## pl(O,2)1:RNAPARP1                                                 
## pl(O,2)2:RNAPARP1                                                 
## p(O,2)1:ALC        0.000                                          
## p(O,2)2:ALC        0.500             0.000                        
## sRNAPARP1:A        0.000             0.000     0.000              
## p(O,2)1:RNAPARP1:  0.000            -0.707     0.000     0.000    
## p(O,2)2:RNAPARP1: -0.707             0.000    -0.707     0.000    
##                   p(O,2)1:RNAPARP1:
## ply(Olp,2)1                        
## ply(Olp,2)2                        
## siRNAsPARP1                        
## gntypALC1KO                        
## pl(O,2)1:RNAPARP1                  
## pl(O,2)2:RNAPARP1                  
## p(O,2)1:ALC                        
## p(O,2)2:ALC                        
## sRNAPARP1:A                        
## p(O,2)1:RNAPARP1:                  
## p(O,2)2:RNAPARP1:  0.000

cat("AIC: ", AIC(fit8))

## AIC:  395.1106

simres <- simulateResiduals(fittedModel = fit8)
plot(simres)

Cubic formula

fit9 <- lm(Counts ~ Experiment + poly(Olaparib,3)*siRNA*genotype, data = dataset)
print(summary(fit9))

## 
## Call:
## lm(formula = Counts ~ Experiment + poly(Olaparib, 3) * siRNA * 
##     genotype, data = dataset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -80.687 -12.203  -2.542  15.125  89.062 
## 
## Coefficients:
##                                                Estimate Std. Error t value
## (Intercept)                                      136.77      15.13   9.038
## Experimentexp2                                    50.69      15.13   3.349
## Experimentexp3                                   105.25      15.13   6.955
## poly(Olaparib, 3)1                              -466.93      85.61  -5.454
## poly(Olaparib, 3)2                              -186.06      85.61  -2.173
## poly(Olaparib, 3)3                                51.35      85.61   0.600
## siRNAsiPARP1                                      12.75      17.48   0.730
## genotypeALC1KO                                  -111.17      17.48  -6.362
## poly(Olaparib, 3)1:siRNAsiPARP1                   93.10     121.07   0.769
## poly(Olaparib, 3)2:siRNAsiPARP1                   63.02     121.07   0.520
## poly(Olaparib, 3)3:siRNAsiPARP1                 -107.17     121.07  -0.885
## poly(Olaparib, 3)1:genotypeALC1KO                  7.95     121.07   0.066
## poly(Olaparib, 3)2:genotypeALC1KO                322.38     121.07   2.663
## poly(Olaparib, 3)3:genotypeALC1KO                -52.55     121.07  -0.434
## siRNAsiPARP1:genotypeALC1KO                       68.50      24.71   2.772
## poly(Olaparib, 3)1:siRNAsiPARP1:genotypeALC1KO   -59.17     171.22  -0.346
## poly(Olaparib, 3)2:siRNAsiPARP1:genotypeALC1KO  -317.01     171.22  -1.851
## poly(Olaparib, 3)3:siRNAsiPARP1:genotypeALC1KO   143.25     171.22   0.837
##                                                Pr(>|t|)    
## (Intercept)                                    4.57e-10 ***
## Experimentexp2                                  0.00220 ** 
## Experimentexp3                                 1.00e-07 ***
## poly(Olaparib, 3)1                             6.46e-06 ***
## poly(Olaparib, 3)2                              0.03778 *  
## poly(Olaparib, 3)3                              0.55309    
## siRNAsiPARP1                                    0.47128    
## genotypeALC1KO                                 5.10e-07 ***
## poly(Olaparib, 3)1:siRNAsiPARP1                 0.44793    
## poly(Olaparib, 3)2:siRNAsiPARP1                 0.60654    
## poly(Olaparib, 3)3:siRNAsiPARP1                 0.38309    
## poly(Olaparib, 3)1:genotypeALC1KO               0.94808    
## poly(Olaparib, 3)2:genotypeALC1KO               0.01234 *  
## poly(Olaparib, 3)3:genotypeALC1KO               0.66733    
## siRNAsiPARP1:genotypeALC1KO                     0.00948 ** 
## poly(Olaparib, 3)1:siRNAsiPARP1:genotypeALC1KO  0.73207    
## poly(Olaparib, 3)2:siRNAsiPARP1:genotypeALC1KO  0.07397 .  
## poly(Olaparib, 3)3:siRNAsiPARP1:genotypeALC1KO  0.40941    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 42.8 on 30 degrees of freedom
## Multiple R-squared:  0.8817, Adjusted R-squared:  0.8147 
## F-statistic: 13.16 on 17 and 30 DF,  p-value: 1.501e-09

cat("AIC: ", AIC(fit9))

## AIC:  512.2951

simres <- simulateResiduals(fittedModel = fit9)
plot(simres)

fit10 <- lm(NormCounts ~ poly(Olaparib,3)*siRNA*genotype, data = dataset)
print(summary(fit10))

## 
## Call:
## lm(formula = NormCounts ~ poly(Olaparib, 3) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.131742 -0.029948  0.002213  0.028289  0.141793 
## 
## Coefficients:
##                                                  Estimate Std. Error t value
## (Intercept)                                     1.000e+00  2.045e-02  48.888
## poly(Olaparib, 3)1                             -2.487e+00  1.417e-01 -17.546
## poly(Olaparib, 3)2                             -1.025e+00  1.417e-01  -7.230
## poly(Olaparib, 3)3                              2.058e-01  1.417e-01   1.452
## siRNAsiPARP1                                   -1.743e-16  2.893e-02   0.000
## genotypeALC1KO                                 -1.705e-16  2.893e-02   0.000
## poly(Olaparib, 3)1:siRNAsiPARP1                 6.382e-01  2.004e-01   3.184
## poly(Olaparib, 3)2:siRNAsiPARP1                 3.941e-01  2.004e-01   1.966
## poly(Olaparib, 3)3:siRNAsiPARP1                -5.388e-01  2.004e-01  -2.688
## poly(Olaparib, 3)1:genotypeALC1KO              -3.401e+00  2.004e-01 -16.967
## poly(Olaparib, 3)2:genotypeALC1KO               2.830e+00  2.004e-01  14.119
## poly(Olaparib, 3)3:genotypeALC1KO              -2.493e-01  2.004e-01  -1.244
## siRNAsiPARP1:genotypeALC1KO                    -4.743e-17  4.091e-02   0.000
## poly(Olaparib, 3)1:siRNAsiPARP1:genotypeALC1KO  2.626e+00  2.834e-01   9.266
## poly(Olaparib, 3)2:siRNAsiPARP1:genotypeALC1KO -2.951e+00  2.834e-01 -10.411
## poly(Olaparib, 3)3:siRNAsiPARP1:genotypeALC1KO  7.294e-01  2.834e-01   2.574
##                                                Pr(>|t|)    
## (Intercept)                                     < 2e-16 ***
## poly(Olaparib, 3)1                              < 2e-16 ***
## poly(Olaparib, 3)2                             3.26e-08 ***
## poly(Olaparib, 3)3                              0.15621    
## siRNAsiPARP1                                    1.00000    
## genotypeALC1KO                                  1.00000    
## poly(Olaparib, 3)1:siRNAsiPARP1                 0.00323 ** 
## poly(Olaparib, 3)2:siRNAsiPARP1                 0.05801 .  
## poly(Olaparib, 3)3:siRNAsiPARP1                 0.01130 *  
## poly(Olaparib, 3)1:genotypeALC1KO               < 2e-16 ***
## poly(Olaparib, 3)2:genotypeALC1KO              2.69e-15 ***
## poly(Olaparib, 3)3:genotypeALC1KO               0.22257    
## siRNAsiPARP1:genotypeALC1KO                     1.00000    
## poly(Olaparib, 3)1:siRNAsiPARP1:genotypeALC1KO 1.41e-10 ***
## poly(Olaparib, 3)2:siRNAsiPARP1:genotypeALC1KO 8.39e-12 ***
## poly(Olaparib, 3)3:siRNAsiPARP1:genotypeALC1KO  0.01490 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07086 on 32 degrees of freedom
## Multiple R-squared:  0.9888, Adjusted R-squared:  0.9835 
## F-statistic: 187.8 on 15 and 32 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit10))

## AIC:  -103.3635

simres <- simulateResiduals(fittedModel = fit10)
plot(simres)

fit11 <- lm(NormCounts2 ~ poly(Olaparib,3)*siRNA*genotype, data = dataset)
print(summary(fit11))

## 
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.085863 -0.019458  0.001602  0.013273  0.098286 
## 
## Coefficients:
##                                                Estimate Std. Error t value
## (Intercept)                                     0.72674    0.01252  58.028
## poly(Olaparib, 3)1                             -1.80709    0.08677 -20.827
## poly(Olaparib, 3)2                             -0.74460    0.08677  -8.582
## poly(Olaparib, 3)3                              0.14955    0.08677   1.724
## siRNAsiPARP1                                    0.03274    0.01771   1.848
## genotypeALC1KO                                 -0.32149    0.01771 -18.151
## poly(Olaparib, 3)1:siRNAsiPARP1                 0.40330    0.12271   3.287
## poly(Olaparib, 3)2:siRNAsiPARP1                 0.26573    0.12271   2.166
## poly(Olaparib, 3)3:siRNAsiPARP1                -0.40247    0.12271  -3.280
## poly(Olaparib, 3)1:genotypeALC1KO              -0.57866    0.12271  -4.716
## poly(Olaparib, 3)2:genotypeALC1KO               1.47615    0.12271  12.030
## poly(Olaparib, 3)3:genotypeALC1KO              -0.16718    0.12271  -1.362
## siRNAsiPARP1:genotypeALC1KO                     0.25518    0.02505  10.188
## poly(Olaparib, 3)1:siRNAsiPARP1:genotypeALC1KO  0.16459    0.17354   0.948
## poly(Olaparib, 3)2:siRNAsiPARP1:genotypeALC1KO -1.51820    0.17354  -8.749
## poly(Olaparib, 3)3:siRNAsiPARP1:genotypeALC1KO  0.52209    0.17354   3.009
##                                                Pr(>|t|)    
## (Intercept)                                     < 2e-16 ***
## poly(Olaparib, 3)1                              < 2e-16 ***
## poly(Olaparib, 3)2                             8.32e-10 ***
## poly(Olaparib, 3)3                              0.09444 .  
## siRNAsiPARP1                                    0.07383 .  
## genotypeALC1KO                                  < 2e-16 ***
## poly(Olaparib, 3)1:siRNAsiPARP1                 0.00246 ** 
## poly(Olaparib, 3)2:siRNAsiPARP1                 0.03791 *  
## poly(Olaparib, 3)3:siRNAsiPARP1                 0.00251 ** 
## poly(Olaparib, 3)1:genotypeALC1KO              4.53e-05 ***
## poly(Olaparib, 3)2:genotypeALC1KO              2.05e-13 ***
## poly(Olaparib, 3)3:genotypeALC1KO               0.18257    
## siRNAsiPARP1:genotypeALC1KO                    1.44e-11 ***
## poly(Olaparib, 3)1:siRNAsiPARP1:genotypeALC1KO  0.35001    
## poly(Olaparib, 3)2:siRNAsiPARP1:genotypeALC1KO 5.37e-10 ***
## poly(Olaparib, 3)3:siRNAsiPARP1:genotypeALC1KO  0.00508 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.04338 on 32 degrees of freedom
## Multiple R-squared:  0.9879, Adjusted R-squared:  0.9823 
## F-statistic: 174.8 on 15 and 32 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit11))

## AIC:  -150.4599

simres <- simulateResiduals(fittedModel = fit11)
plot(simres)

fit12 <- lmer(Counts ~ poly(Olaparib,3)*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit12))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Olaparib, 3) * siRNA * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 325.1
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.57704 -0.47088 -0.06373  0.41746  1.52191 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 3561.7   59.68   
##  Residual              926.6   30.44   
## Number of obs: 48, groups:  UID, 12
## 
## Fixed effects:
##                                                Estimate Std. Error      df
## (Intercept)                                      188.75      35.56    8.00
## poly(Olaparib, 3)1                              -466.93      60.88   24.00
## poly(Olaparib, 3)2                              -186.06      60.88   24.00
## poly(Olaparib, 3)3                                51.35      60.88   24.00
## siRNAsiPARP1                                      12.75      50.29    8.00
## genotypeALC1KO                                  -111.17      50.29    8.00
## poly(Olaparib, 3)1:siRNAsiPARP1                   93.10      86.10   24.00
## poly(Olaparib, 3)2:siRNAsiPARP1                   63.02      86.10   24.00
## poly(Olaparib, 3)3:siRNAsiPARP1                 -107.17      86.10   24.00
## poly(Olaparib, 3)1:genotypeALC1KO                  7.95      86.10   24.00
## poly(Olaparib, 3)2:genotypeALC1KO                322.38      86.10   24.00
## poly(Olaparib, 3)3:genotypeALC1KO                -52.55      86.10   24.00
## siRNAsiPARP1:genotypeALC1KO                       68.50      71.12    8.00
## poly(Olaparib, 3)1:siRNAsiPARP1:genotypeALC1KO   -59.17     121.76   24.00
## poly(Olaparib, 3)2:siRNAsiPARP1:genotypeALC1KO  -317.01     121.76   24.00
## poly(Olaparib, 3)3:siRNAsiPARP1:genotypeALC1KO   143.25     121.76   24.00
##                                                t value Pr(>|t|)    
## (Intercept)                                      5.308 0.000721 ***
## poly(Olaparib, 3)1                              -7.669  6.6e-08 ***
## poly(Olaparib, 3)2                              -3.056 0.005430 ** 
## poly(Olaparib, 3)3                               0.844 0.407262    
## siRNAsiPARP1                                     0.254 0.806247    
## genotypeALC1KO                                  -2.211 0.058028 .  
## poly(Olaparib, 3)1:siRNAsiPARP1                  1.081 0.290314    
## poly(Olaparib, 3)2:siRNAsiPARP1                  0.732 0.471316    
## poly(Olaparib, 3)3:siRNAsiPARP1                 -1.245 0.225243    
## poly(Olaparib, 3)1:genotypeALC1KO                0.092 0.927200    
## poly(Olaparib, 3)2:genotypeALC1KO                3.744 0.001003 ** 
## poly(Olaparib, 3)3:genotypeALC1KO               -0.610 0.547332    
## siRNAsiPARP1:genotypeALC1KO                      0.963 0.363663    
## poly(Olaparib, 3)1:siRNAsiPARP1:genotypeALC1KO  -0.486 0.631416    
## poly(Olaparib, 3)2:siRNAsiPARP1:genotypeALC1KO  -2.603 0.015581 *  
## poly(Olaparib, 3)3:siRNAsiPARP1:genotypeALC1KO   1.176 0.250951    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

cat("AIC: ", AIC(fit12))

## AIC:  361.0916

simres <- simulateResiduals(fittedModel = fit12)
plot(simres)

Compare Results

ICtab(fit1,fit2,fit3,fit4,
      fit5,fit6,fit7,fit8,
      fit9,fit10,fit11,fit12,
      base=T)

##       AIC    dAIC   df
## fit11 -150.5    0.0 17
## fit7  -142.2    8.2 13
## fit10 -103.4   47.1 17
## fit6   -99.7   50.8 13
## fit3   -66.6   83.8 9 
## fit2   -11.4  139.0 9 
## fit12  361.1  511.6 18
## fit8   395.1  545.6 14
## fit4   464.0  614.5 10
## fit5   505.8  656.3 15
## fit9   512.3  662.8 19
## fit1   512.6  663.1 11

Final Result

fit <- fit11

output <- coef(summary(fit))
output <- output[grep("Olaparib", rownames(output)),]

rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype",  paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1],  sep = " in " )

rownames(output) <- gsub("siRNA",  paste0(" ",levels(dataset$siRNA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs| in ", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs| in ", rownames(output)))], levels(dataset$siRNA)[1],  sep = " in " )

rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$siRNA)[1],  sep = " " )


# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
Olaparib1 in WT siCtrl	-1.8070860	0.0867681	-20.8266249	0.0000000
Olaparib2 in WT siCtrl	-0.7446048	0.0867681	-8.5815537	0.0000000
Olaparib3 in WT siCtrl	0.1495508	0.0867681	1.7235695	0.0944365
Olaparib1: siCtrl vs. siPARP1 in WT	0.4032997	0.1227086	3.2866463	0.0024649
Olaparib2: siCtrl vs. siPARP1 in WT	0.2657308	0.1227086	2.1655438	0.0379076
Olaparib3: siCtrl vs. siPARP1 in WT	-0.4024657	0.1227086	-3.2798500	0.0025096
Olaparib1: WT vs. ALC1KO in siCtrl	-0.5786582	0.1227086	-4.7157109	0.0000453
Olaparib2: WT vs. ALC1KO in siCtrl	1.4761548	0.1227086	12.0297605	0.0000000
Olaparib3: WT vs. ALC1KO in siCtrl	-0.1671846	0.1227086	-1.3624523	0.1825682
Olaparib1: siCtrl vs. siPARP1: WT vs. ALC1KO	0.1645883	0.1735361	0.9484384	0.3500144
Olaparib2: siCtrl vs. siPARP1: WT vs. ALC1KO	-1.5182037	0.1735361	-8.7486312	0.0000000
Olaparib3: siCtrl vs. siPARP1: WT vs. ALC1KO	0.5220878	0.1735361	3.0085252	0.0050828

write.table(output, file = "FigureS1E_Stats_Ref_WT.txt", quote = F, sep = "\t", row.names = T, col.names = NA)

# re-fit with ALC1KO reference
dataset$genotype <- relevel(dataset$genotype, ref = "ALC1KO")

fit <- lm(NormCounts2 ~ poly(Olaparib,3)*siRNA*genotype, data = dataset)

output <- coef(summary(fit))
output <- output[grep("Olaparib", rownames(output)),]


rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype",  paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1],  sep = " in " )

rownames(output) <- gsub("siRNA",  paste0(" ",levels(dataset$siRNA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs| in ", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs| in ", rownames(output)))], levels(dataset$siRNA)[1],  sep = " in " )

rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$siRNA)[1],  sep = " " )


# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
Olaparib1 in ALC1KO siCtrl	-2.3857442	0.0867681	-27.4956473	0.0000000
Olaparib2 in ALC1KO siCtrl	0.7315500	0.0867681	8.4310967	0.0000000
Olaparib3 in ALC1KO siCtrl	-0.0176338	0.0867681	-0.2032291	0.8402428
Olaparib1: siCtrl vs. siPARP1 in ALC1KO	0.5678880	0.1227086	4.6279408	0.0000584
Olaparib2: siCtrl vs. siPARP1 in ALC1KO	-1.2524729	0.1227086	-10.2068890	0.0000000
Olaparib3: siCtrl vs. siPARP1 in ALC1KO	0.1196221	0.1227086	0.9748471	0.3369483
Olaparib1: ALC1KO vs. WT in siCtrl	0.5786582	0.1227086	4.7157109	0.0000453
Olaparib2: ALC1KO vs. WT in siCtrl	-1.4761548	0.1227086	-12.0297605	0.0000000
Olaparib3: ALC1KO vs. WT in siCtrl	0.1671846	0.1227086	1.3624523	0.1825682
Olaparib1: siCtrl vs. siPARP1: ALC1KO vs. WT	-0.1645883	0.1735361	-0.9484384	0.3500144
Olaparib2: siCtrl vs. siPARP1: ALC1KO vs. WT	1.5182037	0.1735361	8.7486312	0.0000000
Olaparib3: siCtrl vs. siPARP1: ALC1KO vs. WT	-0.5220878	0.1735361	-3.0085252	0.0050828

write.table(output, file = "FigureS1E_Stats_Ref_ALC1.txt", quote = F, sep = "\t", row.names = T, col.names = NA)

Anova

fit11a <- lm(NormCounts2 ~ poly(Olaparib,3)*siRNA*genotype, data = dataset)
fit11b <- lm(NormCounts2 ~ poly(Olaparib,3)*siRNA+genotype, data = dataset)

# anova table
anova(fit11a, fit11b)

## Analysis of Variance Table
## 
## Model 1: NormCounts2 ~ poly(Olaparib, 3) * siRNA * genotype
## Model 2: NormCounts2 ~ poly(Olaparib, 3) * siRNA + genotype
##   Res.Df     RSS Df Sum of Sq      F    Pr(>F)    
## 1     32 0.06023                                  
## 2     39 0.61070 -7  -0.55047 41.781 2.504e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

fit11c <- lm(NormCounts2 ~ poly(Olaparib,3)*genotype*siRNA, data = dataset)
fit11d <- lm(NormCounts2 ~ poly(Olaparib,3)*genotype+siRNA, data = dataset)

# anova table
anova(fit11c, fit11d)

## Analysis of Variance Table
## 
## Model 1: NormCounts2 ~ poly(Olaparib, 3) * genotype * siRNA
## Model 2: NormCounts2 ~ poly(Olaparib, 3) * genotype + siRNA
##   Res.Df     RSS Df Sum of Sq      F    Pr(>F)    
## 1     32 0.06023                                  
## 2     39 0.54317 -7  -0.48294 36.655 1.581e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

New analysis

• fit model for groups (genotype+siRNA)

dataset$GSID <- factor(dataset$GSID, levels = unique(dataset$GSID))

GSID_levels <- levels(dataset$GSID)

for(g in seq_along(GSID_levels)){
        
        cat("Group: ", GSID_levels[g], "\n")
        
        dataset$GSID <- factor(dataset$GSID, levels = unique(dataset$GSID))
        dataset$GSID <- relevel(dataset$GSID, ref = GSID_levels[g])
        
        fit_group <- lm(NormCounts2 ~ poly(Olaparib,3)*GSID, data = dataset)
        
        print(summary(fit_group))
        cat("AIC: ", AIC(fit_group))
        
        
        simres <- simulateResiduals(fittedModel = fit_group)
        plot(simres)
        
        if(g == 1){
                output <- coef(summary(fit_group))
                output <- output[grep("Olaparib", rownames(output)),]
                rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output))
                
                rownames(output) <- gsub("GSID",  paste0(" ",GSID_levels[g], " vs. "), rownames(output))
                rownames(output)[!(grepl("vs", rownames(output)))]  <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$GSID)[1],  sep = " in " )

                
        } else {
                outtmp <- coef(summary(fit_group))
                outtmp <- outtmp[grep("Olaparib", rownames(outtmp)),]
                rownames(outtmp) <- gsub("poly\\(|, [1-3]\\)","", rownames(outtmp))
                
                rownames(outtmp) <- gsub("GSID",  paste0(" ",GSID_levels[g], " vs. "), rownames(outtmp))
                rownames(outtmp)[!(grepl("vs", rownames(outtmp)))]  <- paste(rownames(outtmp)[!(grepl("vs", rownames(outtmp)))], levels(dataset$GSID)[1],  sep = " in " )

                
                output <- rbind(output,outtmp)
        }
        
        if(g < length(GSID_levels)){
                
                output <- rbind(output, " ", colnames(output))
        }
        
}

## Group:  WT siCtrl 
## 
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * GSID, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.085863 -0.019458  0.001602  0.013273  0.098286 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                            0.72674    0.01252  58.028  < 2e-16 ***
## poly(Olaparib, 3)1                    -1.80709    0.08677 -20.827  < 2e-16 ***
## poly(Olaparib, 3)2                    -0.74460    0.08677  -8.582 8.32e-10 ***
## poly(Olaparib, 3)3                     0.14955    0.08677   1.724  0.09444 .  
## GSIDWT siPARP1                         0.03274    0.01771   1.848  0.07383 .  
## GSIDALC1KO siCtrl                     -0.32149    0.01771 -18.151  < 2e-16 ***
## GSIDALC1KO siPARP1                    -0.03358    0.01771  -1.896  0.06706 .  
## poly(Olaparib, 3)1:GSIDWT siPARP1      0.40330    0.12271   3.287  0.00246 ** 
## poly(Olaparib, 3)2:GSIDWT siPARP1      0.26573    0.12271   2.166  0.03791 *  
## poly(Olaparib, 3)3:GSIDWT siPARP1     -0.40247    0.12271  -3.280  0.00251 ** 
## poly(Olaparib, 3)1:GSIDALC1KO siCtrl  -0.57866    0.12271  -4.716 4.53e-05 ***
## poly(Olaparib, 3)2:GSIDALC1KO siCtrl   1.47615    0.12271  12.030 2.05e-13 ***
## poly(Olaparib, 3)3:GSIDALC1KO siCtrl  -0.16718    0.12271  -1.362  0.18257    
## poly(Olaparib, 3)1:GSIDALC1KO siPARP1 -0.01077    0.12271  -0.088  0.93061    
## poly(Olaparib, 3)2:GSIDALC1KO siPARP1  0.22368    0.12271   1.823  0.07768 .  
## poly(Olaparib, 3)3:GSIDALC1KO siPARP1 -0.04756    0.12271  -0.388  0.70087    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.04338 on 32 degrees of freedom
## Multiple R-squared:  0.9879, Adjusted R-squared:  0.9823 
## F-statistic: 174.8 on 15 and 32 DF,  p-value: < 2.2e-16
## 
## AIC:  -150.4599

## Group:  WT siPARP1 
## 
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * GSID, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.085863 -0.019458  0.001602  0.013273  0.098286 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                            0.75947    0.01252  60.642  < 2e-16 ***
## poly(Olaparib, 3)1                    -1.40379    0.08677 -16.179  < 2e-16 ***
## poly(Olaparib, 3)2                    -0.47887    0.08677  -5.519 4.39e-06 ***
## poly(Olaparib, 3)3                    -0.25291    0.08677  -2.915 0.006448 ** 
## GSIDWT siCtrl                         -0.03274    0.01771  -1.848 0.073827 .  
## GSIDALC1KO siCtrl                     -0.35422    0.01771 -20.000  < 2e-16 ***
## GSIDALC1KO siPARP1                    -0.06631    0.01771  -3.744 0.000715 ***
## poly(Olaparib, 3)1:GSIDWT siCtrl      -0.40330    0.12271  -3.287 0.002465 ** 
## poly(Olaparib, 3)2:GSIDWT siCtrl      -0.26573    0.12271  -2.166 0.037908 *  
## poly(Olaparib, 3)3:GSIDWT siCtrl       0.40247    0.12271   3.280 0.002510 ** 
## poly(Olaparib, 3)1:GSIDALC1KO siCtrl  -0.98196    0.12271  -8.002 3.90e-09 ***
## poly(Olaparib, 3)2:GSIDALC1KO siCtrl   1.21042    0.12271   9.864 3.16e-11 ***
## poly(Olaparib, 3)3:GSIDALC1KO siCtrl   0.23528    0.12271   1.917 0.064151 .  
## poly(Olaparib, 3)1:GSIDALC1KO siPARP1 -0.41407    0.12271  -3.374 0.001952 ** 
## poly(Olaparib, 3)2:GSIDALC1KO siPARP1 -0.04205    0.12271  -0.343 0.734086    
## poly(Olaparib, 3)3:GSIDALC1KO siPARP1  0.35490    0.12271   2.892 0.006825 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.04338 on 32 degrees of freedom
## Multiple R-squared:  0.9879, Adjusted R-squared:  0.9823 
## F-statistic: 174.8 on 15 and 32 DF,  p-value: < 2.2e-16
## 
## AIC:  -150.4599

## Group:  ALC1KO siCtrl 
## 
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * GSID, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.085863 -0.019458  0.001602  0.013273  0.098286 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                            0.40525    0.01252  32.358  < 2e-16 ***
## poly(Olaparib, 3)1                    -2.38574    0.08677 -27.496  < 2e-16 ***
## poly(Olaparib, 3)2                     0.73155    0.08677   8.431 1.24e-09 ***
## poly(Olaparib, 3)3                    -0.01763    0.08677  -0.203   0.8402    
## GSIDWT siCtrl                          0.32149    0.01771  18.151  < 2e-16 ***
## GSIDWT siPARP1                         0.35422    0.01771  20.000  < 2e-16 ***
## GSIDALC1KO siPARP1                     0.28791    0.01771  16.256  < 2e-16 ***
## poly(Olaparib, 3)1:GSIDWT siCtrl       0.57866    0.12271   4.716 4.53e-05 ***
## poly(Olaparib, 3)2:GSIDWT siCtrl      -1.47615    0.12271 -12.030 2.05e-13 ***
## poly(Olaparib, 3)3:GSIDWT siCtrl       0.16718    0.12271   1.362   0.1826    
## poly(Olaparib, 3)1:GSIDWT siPARP1      0.98196    0.12271   8.002 3.90e-09 ***
## poly(Olaparib, 3)2:GSIDWT siPARP1     -1.21042    0.12271  -9.864 3.16e-11 ***
## poly(Olaparib, 3)3:GSIDWT siPARP1     -0.23528    0.12271  -1.917   0.0642 .  
## poly(Olaparib, 3)1:GSIDALC1KO siPARP1  0.56789    0.12271   4.628 5.84e-05 ***
## poly(Olaparib, 3)2:GSIDALC1KO siPARP1 -1.25247    0.12271 -10.207 1.37e-11 ***
## poly(Olaparib, 3)3:GSIDALC1KO siPARP1  0.11962    0.12271   0.975   0.3369    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.04338 on 32 degrees of freedom
## Multiple R-squared:  0.9879, Adjusted R-squared:  0.9823 
## F-statistic: 174.8 on 15 and 32 DF,  p-value: < 2.2e-16
## 
## AIC:  -150.4599

## Group:  ALC1KO siPARP1 
## 
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * GSID, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.085863 -0.019458  0.001602  0.013273  0.098286 
## 
## Coefficients:
##                                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                           0.69316    0.01252  55.347  < 2e-16 ***
## poly(Olaparib, 3)1                   -1.81786    0.08677 -20.951  < 2e-16 ***
## poly(Olaparib, 3)2                   -0.52092    0.08677  -6.004 1.07e-06 ***
## poly(Olaparib, 3)3                    0.10199    0.08677   1.175 0.248501    
## GSIDWT siCtrl                         0.03358    0.01771   1.896 0.067063 .  
## GSIDWT siPARP1                        0.06631    0.01771   3.744 0.000715 ***
## GSIDALC1KO siCtrl                    -0.28791    0.01771 -16.256  < 2e-16 ***
## poly(Olaparib, 3)1:GSIDWT siCtrl      0.01077    0.12271   0.088 0.930606    
## poly(Olaparib, 3)2:GSIDWT siCtrl     -0.22368    0.12271  -1.823 0.077678 .  
## poly(Olaparib, 3)3:GSIDWT siCtrl      0.04756    0.12271   0.388 0.700875    
## poly(Olaparib, 3)1:GSIDWT siPARP1     0.41407    0.12271   3.374 0.001952 ** 
## poly(Olaparib, 3)2:GSIDWT siPARP1     0.04205    0.12271   0.343 0.734086    
## poly(Olaparib, 3)3:GSIDWT siPARP1    -0.35490    0.12271  -2.892 0.006825 ** 
## poly(Olaparib, 3)1:GSIDALC1KO siCtrl -0.56789    0.12271  -4.628 5.84e-05 ***
## poly(Olaparib, 3)2:GSIDALC1KO siCtrl  1.25247    0.12271  10.207 1.37e-11 ***
## poly(Olaparib, 3)3:GSIDALC1KO siCtrl -0.11962    0.12271  -0.975 0.336948    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.04338 on 32 degrees of freedom
## Multiple R-squared:  0.9879, Adjusted R-squared:  0.9823 
## F-statistic: 174.8 on 15 and 32 DF,  p-value: < 2.2e-16
## 
## AIC:  -150.4599

# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
Olaparib1 in WT siCtrl	-1.8070860260776	0.0867680687886442	-20.8266249474727	3.56837845460282e-20
Olaparib2 in WT siCtrl	-0.744604844400641	0.0867680687886442	-8.58155373049045	8.3152942771629e-10
Olaparib3 in WT siCtrl	0.149550794521518	0.0867680687886442	1.72356947215	0.094436460196784
Olaparib1: WT siCtrl vs. WT siPARP1	0.40329970462228	0.122708579661822	3.28664634317951	0.00246492782526375
Olaparib2: WT siCtrl vs. WT siPARP1	0.265730806847873	0.122708579661822	2.16554382407661	0.0379076081403378
Olaparib3: WT siCtrl vs. WT siPARP1	-0.402465737028792	0.122708579661822	-3.27985001650222	0.00250963486123093
Olaparib1: WT siCtrl vs. ALC1KO siCtrl	-0.578658188326406	0.122708579661822	-4.71571091378577	4.53202364757456e-05
Olaparib2: WT siCtrl vs. ALC1KO siCtrl	1.4761548220362	0.122708579661822	12.0297604788874	2.0539478163982e-13
Olaparib3: WT siCtrl vs. ALC1KO siCtrl	-0.167184587439239	0.122708579661822	-1.36245230692091	0.182568168211289
Olaparib1: WT siCtrl vs. ALC1KO siPARP1	-0.0107701456722134	0.122708579661822	-0.0877701111193306	0.930606362706263
Olaparib2: WT siCtrl vs. ALC1KO siPARP1	0.223681968066194	0.122708579661822	1.82287146247352	0.0776781922690946
Olaparib3: WT siCtrl vs. ALC1KO siPARP1	-0.0475624790397321	0.122708579661822	-0.387605163150055	0.700874528002675
	Estimate	Std. Error	t value	Pr(>|t|)
Olaparib1 in WT siPARP1	-1.40378632145532	0.0867680687886443	-16.1786051142243	5.81149956205918e-17
Olaparib2 in WT siPARP1	-0.478874037552769	0.0867680687886443	-5.51901228456799	4.38704573342403e-06
Olaparib3 in WT siPARP1	-0.252914942507273	0.0867680687886443	-2.91483890373706	0.00644783187258956
Olaparib1: WT siPARP1 vs. WT siCtrl	-0.40329970462228	0.122708579661822	-3.28664634317951	0.00246492782526375
Olaparib2: WT siPARP1 vs. WT siCtrl	-0.265730806847873	0.122708579661822	-2.16554382407661	0.0379076081403379
Olaparib3: WT siPARP1 vs. WT siCtrl	0.402465737028792	0.122708579661822	3.27985001650222	0.00250963486123093
Olaparib1: WT siPARP1 vs. ALC1KO siCtrl	-0.981957892948686	0.122708579661822	-8.00235725696527	3.90057672915589e-09
Olaparib2: WT siPARP1 vs. ALC1KO siCtrl	1.21042401518832	0.122708579661822	9.86421665481077	3.16430719712426e-11
Olaparib3: WT siPARP1 vs. ALC1KO siCtrl	0.235281149589553	0.122708579661822	1.9173977095813	0.0641512738454839
Olaparib1: WT siPARP1 vs. ALC1KO siPARP1	-0.414069850294493	0.122708579661822	-3.37441645429884	0.00195194161343676
Olaparib2: WT siPARP1 vs. ALC1KO siPARP1	-0.0420488387816784	0.122708579661822	-0.342672361603097	0.734085758416724
Olaparib3: WT siPARP1 vs. ALC1KO siPARP1	0.35490325798906	0.122708579661822	2.89224485335216	0.00682527691914667
	Estimate	Std. Error	t value	Pr(>|t|)
Olaparib1 in ALC1KO siCtrl	-2.38574421440401	0.0867680687886442	-27.4956472779793	7.80408902985544e-24
Olaparib2 in ALC1KO siCtrl	0.731549977635554	0.0867680687886443	8.43109669085196	1.23731802786328e-09
Olaparib3 in ALC1KO siCtrl	-0.01763379291772	0.0867680687886442	-0.203229058384066	0.840242795391438
Olaparib1: ALC1KO siCtrl vs. WT siCtrl	0.578658188326407	0.122708579661822	4.71571091378577	4.53202364757452e-05
Olaparib2: ALC1KO siCtrl vs. WT siCtrl	-1.4761548220362	0.122708579661822	-12.0297604788874	2.05394781639819e-13
Olaparib3: ALC1KO siCtrl vs. WT siCtrl	0.167184587439238	0.122708579661822	1.36245230692091	0.182568168211289
Olaparib1: ALC1KO siCtrl vs. WT siPARP1	0.981957892948685	0.122708579661822	8.00235725696527	3.90057672915593e-09
Olaparib2: ALC1KO siCtrl vs. WT siPARP1	-1.21042401518832	0.122708579661822	-9.86421665481078	3.16430719712424e-11
Olaparib3: ALC1KO siCtrl vs. WT siPARP1	-0.235281149589553	0.122708579661822	-1.91739770958131	0.0641512738454837
Olaparib1: ALC1KO siCtrl vs. ALC1KO siPARP1	0.567888042654192	0.122708579661822	4.62794080266644	5.84009600713273e-05
Olaparib2: ALC1KO siCtrl vs. ALC1KO siPARP1	-1.25247285397	0.122708579661822	-10.2068890164139	1.370407310815e-11
Olaparib3: ALC1KO siCtrl vs. ALC1KO siPARP1	0.119622108399506	0.122708579661822	0.974847143770854	0.336948267843165
	Estimate	Std. Error	t value	Pr(>|t|)
Olaparib1 in ALC1KO siPARP1	-1.81785617174982	0.0867680687886443	-20.9507506289886	2.9879004313695e-20
Olaparib2 in ALC1KO siPARP1	-0.520922876334447	0.0867680687886443	-6.00362418579751	1.07410765578824e-06
Olaparib3 in ALC1KO siPARP1	0.101988315481786	0.0867680687886442	1.17541299357735	0.248501265566575
Olaparib1: ALC1KO siPARP1 vs. WT siCtrl	0.0107701456722135	0.122708579661822	0.0877701111193314	0.930606362706263
Olaparib2: ALC1KO siPARP1 vs. WT siCtrl	-0.223681968066194	0.122708579661822	-1.82287146247352	0.077678192269095
Olaparib3: ALC1KO siPARP1 vs. WT siCtrl	0.0475624790397322	0.122708579661822	0.387605163150055	0.700874528002675
Olaparib1: ALC1KO siPARP1 vs. WT siPARP1	0.414069850294493	0.122708579661822	3.37441645429884	0.00195194161343675
Olaparib2: ALC1KO siPARP1 vs. WT siPARP1	0.0420488387816784	0.122708579661822	0.342672361603097	0.734085758416724
Olaparib3: ALC1KO siPARP1 vs. WT siPARP1	-0.35490325798906	0.122708579661822	-2.89224485335216	0.00682527691914668
Olaparib1: ALC1KO siPARP1 vs. ALC1KO siCtrl	-0.567888042654193	0.122708579661822	-4.62794080266644	5.84009600713276e-05
Olaparib2: ALC1KO siPARP1 vs. ALC1KO siCtrl	1.25247285397	0.122708579661822	10.2068890164139	1.37040731081499e-11
Olaparib3: ALC1KO siPARP1 vs. ALC1KO siCtrl	-0.119622108399506	0.122708579661822	-0.974847143770858	0.336948267843163

write.table(output, file = "FigureS1E_Stats_New_All.txt", quote = F, sep = "\t", row.names = T, col.names = NA)